Bibliographic record
Abstract
AERAC is a process-based identity framework that explains how personal identity forms, stabilizes, fractures, and recovers under relational and systemic pressure. Rather than treating identity as a fixed trait, narrative, or diagnosis, the model conceptualizes identity as a dynamic system maintained through ongoing interaction between internal structure and external response. AERAC identifies five interacting components—Anchor, Echo, Resonance, Agency, and Cognition—that together account for coherence, instability, resilience, and breakdown without reducing individuals to pathology or absolving them of responsibility . The model integrates internal continuity (Anchor), outward expression over time (Echo), environmental feedback (Resonance), choice under constraint (Agency), and meaning-making processes (Cognition) into a single functional loop. Identity coherence emerges when these components remain aligned and integrated; fragmentation occurs when one or more elements—most critically agency or cognition—are chronically disrupted. Importantly, AERAC treats resonance as morally neutral and emphasizes that silence, distortion, or incoherent feedback can be as destabilizing as overt rejection, particularly in contexts of chronic misrecognition or power asymmetry . AERAC preserves accountability without demonization by locating behavior at the level of agency while acknowledging the constraining effects of trauma, illness, and systemic pressure. This allows harmful behavior to be explained without excused, and suffering to be understood without collapsing into victimhood. The framework is applicable across psychology, trauma and recovery work, identity development, leadership studies, and the analysis of abusive or destabilizing relational and institutional environments, offering a non-pathologizing language for both breakdown and repair
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".